Black Widow Optimization Algorithm-Based Sub Synchronous Resonance Investigation with Series Compensation

Abstract

The integration of wind farms into the transmission network has raised various concerns and objections. To address these challenges, the deployment of series compensators has gained significance as an effective method for enhancing the power transmission capacity of existing lines, particularly for the integration of large-scale wind farms. However, the use of series reactive compensation lines can lead to a phenomenon known as sub-synchronous resonance (SSR) in turbo generators. This SSR can cause electrical instability at sub-synchronous frequencies and potential damage to the turbine’s generator shaft, particularly during periods of peak production. As a result, the assessment of SSR through comprehensive surveys is crucial for wind power engineers to make informed decisions and ensure the safety of turbines. This study explores the potential of SSR through the utilization of the black widow optimization technique. The analysis takes into account variables such as variations in the speed of the wind, compensation spectrum, and wind velocity and examines the eigenvalue and involvement components under various operating situations. To validate our findings, electromagnetic transient simulations are conducted using the PSCAD/EMTDC software, employing the initial IEEE reference systems. This research contributes to a deeper understanding of SSR and its implications for wind energy systems.

Keywords:

• Black Widow Optimization (BWO)
• the transmission network
• wind power
• signal analysis
• renewable energy sources

ACKNOWLEDGEMENT

There is no acknowledgement involved in this work.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Ethics Approval and Consent to Participate

No participation of humans takes place in this implementation process.

Human and Animal Rights

No violation of Human and Animal Rights is involved.

Authorship Contributions

All authors are contributed equally to this work.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

No funding is involved in this work.

Notes on contributors

S. Arockiaraj

S. Arockiaraj was born in the year 1988. He has received his B.E degree in Electrical and Electronics Engineering and Masters in Power Systems Engineering in the year 2011 and 2013 respectively. He has been working as an Assistant professor at Mepco Schlenk Engineering in the department of Electrical and Electronics Engineering since 2013 and he has almost 10 years of experience in the teaching field. He has published 12 journals including science citation. He is currently pursuing Ph.D in Anna University. His subject of interest includes Power systems engineering, Electrical Machines, Electrical Drives and Control, Control Systems, Smart grid, Machine Learning and Data Science.

B. V. Manikandan

B. V. Manikandan presently working as Senior Professor in the Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, TamilNadu State, India. He obtained his B.E., degree in Electrical and Electronics Engineering and M.E., degree in Power systems Engineering during May 1990 and November 1991 from Madurai Kamaraj University. He received his Ph.D degree from Anna University, Chennai during the year 2009. He is having nearly 30 years of teaching experience and more than 25 eras of research experience. He has to his credit more than 55 articles published International journals. His main contributions are in the areas of Power System Restructuring, Application of FACT S Devices, Smart Grid, Power Converters and Controllers for Drives.